1 Introduction
You can install the complete tidyverse with a single line of code:
install.packages("tidyverse")Once you have installed the package, you can load it with the library() function:
library(tidyverse)Now install the nycflights13 dataset with this command
install.packages("nycflights13") Let’s preview the datasets from the nycflights13 packages.
Type following code in your r script and run the code
require(nycflights13)
airlines
airports
planes
flights
weatherHere’s the following output
require(nycflights13)
airlines## # A tibble: 16 x 2
## carrier name
## <chr> <chr>
## 1 9E Endeavor Air Inc.
## 2 AA American Airlines Inc.
## 3 AS Alaska Airlines Inc.
## 4 B6 JetBlue Airways
## 5 DL Delta Air Lines Inc.
## 6 EV ExpressJet Airlines Inc.
## 7 F9 Frontier Airlines Inc.
## 8 FL AirTran Airways Corporation
## 9 HA Hawaiian Airlines Inc.
## 10 MQ Envoy Air
## 11 OO SkyWest Airlines Inc.
## 12 UA United Air Lines Inc.
## 13 US US Airways Inc.
## 14 VX Virgin America
## 15 WN Southwest Airlines Co.
## 16 YV Mesa Airlines Inc.airports## # A tibble: 1,458 x 8
## faa name lat lon alt tz
## <chr> <chr> <dbl> <dbl> <int> <dbl>
## 1 04G Lansdowne Airport 41.13047 -80.61958 1044 -5
## 2 06A Moton Field Municipal Airport 32.46057 -85.68003 264 -6
## 3 06C Schaumburg Regional 41.98934 -88.10124 801 -6
## 4 06N Randall Airport 41.43191 -74.39156 523 -5
## 5 09J Jekyll Island Airport 31.07447 -81.42778 11 -5
## 6 0A9 Elizabethton Municipal Airport 36.37122 -82.17342 1593 -5
## 7 0G6 Williams County Airport 41.46731 -84.50678 730 -5
## 8 0G7 Finger Lakes Regional Airport 42.88356 -76.78123 492 -5
## 9 0P2 Shoestring Aviation Airfield 39.79482 -76.64719 1000 -5
## 10 0S9 Jefferson County Intl 48.05381 -122.81064 108 -8
## # ... with 1,448 more rows, and 2 more variables: dst <chr>, tzone <chr>planes## # A tibble: 3,322 x 9
## tailnum year type manufacturer model
## <chr> <int> <chr> <chr> <chr>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-145XR
## 2 N102UW 1998 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 3 N103US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 4 N104UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 5 N10575 2002 Fixed wing multi engine EMBRAER EMB-145LR
## 6 N105UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 7 N107US 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 8 N108UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 9 N109UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## 10 N110UW 1999 Fixed wing multi engine AIRBUS INDUSTRIE A320-214
## # ... with 3,312 more rows, and 4 more variables: engines <int>,
## # seats <int>, speed <int>, engine <chr>flights## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # ... with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>weather## # A tibble: 26,130 x 15
## origin year month day hour temp dewp humid wind_dir wind_speed
## <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EWR 2013 1 1 0 37.04 21.92 53.97 230 10.35702
## 2 EWR 2013 1 1 1 37.04 21.92 53.97 230 13.80936
## 3 EWR 2013 1 1 2 37.94 21.92 52.09 230 12.65858
## 4 EWR 2013 1 1 3 37.94 23.00 54.51 230 13.80936
## 5 EWR 2013 1 1 4 37.94 24.08 57.04 240 14.96014
## 6 EWR 2013 1 1 6 39.02 26.06 59.37 270 10.35702
## 7 EWR 2013 1 1 7 39.02 26.96 61.63 250 8.05546
## 8 EWR 2013 1 1 8 39.02 28.04 64.43 240 11.50780
## 9 EWR 2013 1 1 9 39.92 28.04 62.21 250 12.65858
## 10 EWR 2013 1 1 10 39.02 28.04 64.43 260 12.65858
## # ... with 26,120 more rows, and 5 more variables: wind_gust <dbl>,
## # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>To get useful metadata on the airlines data set, type
help(airlines)A help page in RStudio appears providing metatdata on the airlines data set
Do the same for airports, planes, flights and weather